Universal Problem Solvers Making Self-Improvements

Title: Universal Problem Solvers Making Self-Improvements

Research Question: Can self-referential problem solvers be designed to solve arbitrary computation problems, such as maximizing a robot's future reward in a reactive environment, by making provably optimal self-improvements?

Methodology: The researchers propose a self-referential problem solver called a Gödel machine. This machine is designed to solve problems by rewriting its own software as soon as it finds a proof that this will improve its future performance. This approach allows for globally optimal self-improvements, as proven by the researchers.

Results: The researchers present a detailed model of a Gödel machine, including its hardware, initial software, and a proof searcher that connects syntax and semantics. They demonstrate that this machine can make provably optimal self-improvements using a bias-optimal proof search (BIOPS) strategy.

Implications: The research suggests that self-referential problem solvers can be designed to solve a wide range of problems by making provably optimal self-improvements. This could have significant implications for the field of computer science and artificial intelligence, potentially leading to more efficient and adaptable problem solvers.

Summary: This research proposes a novel approach to problem solving using self-referential problem solvers, specifically a Gödel machine. The machine is designed to make provably optimal self-improvements, which could have significant implications for the field of computer science and artificial intelligence.

Link to Article: https://arxiv.org/abs/0309048v2 Authors: arXiv ID: 0309048v2